Domain shift between source and target data causes performance degradation. To address this problem we propose a two-step domain adaptation for SSDA. The proposed approach consists of a cross-domain adaptation network and an intra-domain adaptation and hence is termed dual-domain adaptation. The cross-domain part aims to close cross-domain shift, while the intra-domain part addresses intra-domain discrepancy generated by the cross-domain part.
The cross-domain shift is addressed by training a single model on both labeled source data \(D_{s}\)  and labeled target data \(D_{t}\) . The intra-domain shift is addressed by utilizing unlabeled target data \(D_{u}\) . An overview of the proposed dual SSDA framework is shown in Figure REF . Note that we only show the ComplexMix based framwork but don't show ClassMix based framework which is similar to ComplexMix based framework. Our framework consists of two main components: a student-teacher network for intra-domain adaptation with a teacher (upper branch) and a student (middle branch), and a second student network for cross-domain adaptation (lower branch) that shares weights with the the intra-domain student network (middle branch). The proposed approach follows a Mean Teacher Framework [1]}, where the teacher network blends its parameters with the student network parameters using an exponential moving average (EMA). Details of the main components are provided below.
